Tune data for experimental purpose

The error can be reduced and sample size increased for specific purpose.

Usage

tune_Data(data, decrease.error = 0, increase.data = 0)

Arguments

data

data.frame (required): input values, structure: data (values[,1]) and data error (values [,2]) are required

decrease.error

numeric: factor by which the error is decreased, ranges between 0 and 1.

increase.data

numeric: factor by which the error is decreased, ranges between 0 and Inf.

Value

Returns a data.frame with tuned values.

Note

You should not use this function to improve your poor data set!

Function version

0.5.0

Author

Michael Dietze, GFZ Potsdam (Germany) , RLum Developer Team

How to cite

Dietze, M., 2024. tune_Data(): Tune data for experimental purpose. Function version 0.5.0. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.26. https://r-lum.github.io/Luminescence/

Examples

## load example data set
data(ExampleData.DeValues, envir = environment())
x <- ExampleData.DeValues$CA1

## plot original data
plot_AbanicoPlot(data = x,
                 summary = c("n", "mean"))


## decrease error by 10 %
plot_AbanicoPlot(data = tune_Data(x, decrease.error = 0.1),
                 summary = c("n", "mean"))
#> Warning: Dear runner, these activities on your Linux machine have been tracked and will be submitted to the R.Lum data base. Cheating does not pay off! [2024-11-18 13:34:19.221047]


## increase sample size by 200 %
#plot_AbanicoPlot(data = tune_Data(x, increase.data = 2) ,
#                summary = c("n", "mean"))